# Copyright 2023-2024 SGLang Team
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================

from __future__ import annotations

import logging
import math
import time
from abc import ABC
from collections import deque
from contextlib import contextmanager
from pathlib import Path
from typing import TYPE_CHECKING, Any, Dict, List, Literal, Optional, Tuple, Type

import einops
import torch
import torch.distributed

from sglang.srt.environ import envs
from sglang.srt.metrics.collector import ExpertDispatchCollector
from sglang.srt.model_executor.forward_batch_info import ForwardBatch
from sglang.srt.server_args import ServerArgs
from sglang.srt.utils import Withable, get_int_env_var

if TYPE_CHECKING:
    from sglang.srt.eplb.expert_location import ExpertLocationMetadata

logger = logging.getLogger(__name__)

# --------------------------------------- Entrypoint -----------------------------------------

_OutputMode = Literal["file", "object"]


class ExpertDistributionRecorder(ABC):
    """Global expert distribution recording"""

    @staticmethod
    def init_new(
        server_args: ServerArgs,
        expert_location_metadata: ExpertLocationMetadata,
        rank: int,
    ):
        if server_args.expert_distribution_recorder_mode is not None:
            assert (
                expert_location_metadata is not None
            ), "ExpertLocationMetadata is required for expert distribution recording. One possible"
            "reason is that you are using a model that does not support expert distribution"
            "recording. Try setting `get_model_config_for_expert_location` in your model."
            return _ExpertDistributionRecorderReal(
                server_args, expert_location_metadata, rank
            )
        else:
            return _ExpertDistributionRecorderNoop()

    @contextmanager
    def with_current_layer(self, layer_idx):
        yield

    @contextmanager
    def with_debug_name(self, debug_name):
        yield

    @contextmanager
    def disable_this_region(self):
        yield

    @contextmanager
    def with_forward_pass(self, forward_pass_id: int, forward_batch: ForwardBatch):
        yield

    def on_select_experts(self, topk_ids: torch.Tensor):
        pass

    def on_deepep_dispatch_normal(
        self,
        local_physical_count_of_layer: List[int],
        num_tokens_per_rank,
        num_tokens_per_rdma_rank,
        num_tokens_per_expert,
    ):
        pass

    def on_deepep_dispatch_low_latency(
        self, local_physical_count_of_layer: torch.Tensor
    ):
        pass

    def start_record(self):
        self._on_not_implemented()

    def stop_record(self):
        self._on_not_implemented()

    def dump_record(self, output_mode: _OutputMode = "file"):
        self._on_not_implemented()

    @property
    def recording(self):
        return False

    def _on_not_implemented(self):
        raise Exception(
            "Please set ServerArgs.expert_distribution_recorder_mode to use ExpertDistributionRecorder."
        )


class _ExpertDistributionRecorderNoop(ExpertDistributionRecorder):
    pass


class _ExpertDistributionRecorderReal(ExpertDistributionRecorder):
    def __init__(
        self,
        server_args: ServerArgs,
        expert_location_metadata: ExpertLocationMetadata,
        rank: int,
    ):
        self._server_args = server_args
        self._expert_location_metadata = expert_location_metadata

        self._recording = False
        self._disable_all = False
        self._current_forward_pass_id = Withable()
        self._current_layer_idx = Withable()
        self._current_debug_name = Withable()
        self._accumulator = _Accumulator.init_new(
            server_args, expert_location_metadata, rank
        )
        self._single_pass_gatherers = {
            k: _SinglePassGatherer.init_new(server_args, expert_location_metadata, rank)
            for k in self._accumulator.get_single_pass_gatherer_keys()
        }

        if server_args.enable_expert_distribution_metrics:
            logger.info(
                "ExpertDistributionRecorder auto start record since enable_expert_distribution_metrics"
            )
            self.start_record()

    def with_current_layer(self, layer_idx):
        return self._current_layer_idx.with_value(layer_idx)

    def with_debug_name(self, debug_name):
        return self._current_debug_name.with_value(debug_name)

    @contextmanager
    def with_forward_pass(self, forward_pass_id: int, forward_batch: ForwardBatch):
        with self._current_forward_pass_id.with_value(forward_pass_id):
            self._on_forward_pass_start(forward_batch)
            try:
                yield
            finally:
                self._on_forward_pass_end(forward_pass_id)

    @contextmanager
    def disable_this_region(self):
        """Context manager to temporarily disable recording."""
        previous_disable_all = self._disable_all
        self._disable_all = True
        try:
            yield
        finally:
            self._disable_all = previous_disable_all

    def _on_forward_pass_start(self, forward_batch: ForwardBatch):
        if not self._recording:
            return
        for gatherer_key, gatherer in self._single_pass_gatherers.items():
            gatherer.reset()
            gatherer.on_forward_pass_start(forward_batch)

    def _on_forward_pass_end(self, forward_pass_id: int):
        if not self._recording:
            return
        for gatherer_key, gatherer in self._single_pass_gatherers.items():
            single_pass_data = gatherer.collect()
            self._accumulator.append(forward_pass_id, gatherer_key, single_pass_data)

    def on_select_experts(self, topk_ids: torch.Tensor):
        self._on_hook("on_select_experts", topk_ids=topk_ids)

    def on_deepep_dispatch_normal(
        self,
        local_physical_count_of_layer: List[int],
        num_tokens_per_rank,
        num_tokens_per_rdma_rank,
        num_tokens_per_expert,
    ):
        self._on_hook(
            "on_deepep_dispatch_normal",
            local_physical_count_of_layer=local_physical_count_of_layer,
            num_tokens_per_rank=num_tokens_per_rank,
            num_tokens_per_rdma_rank=num_tokens_per_rdma_rank,
            num_tokens_per_expert=num_tokens_per_expert,
        )

    def on_deepep_dispatch_low_latency(
        self, local_physical_count_of_layer: torch.Tensor
    ):
        self._on_hook(
            "on_deepep_dispatch_low_latency",
            local_physical_count_of_layer=local_physical_count_of_layer,
        )

    def _on_hook(self, hook_name: str, **kwargs):
        if self._disable_all:
            return
        if not (
            self._recording or torch.get_device_module().is_current_stream_capturing()
        ):
            return
        gatherer = self._single_pass_gatherers[
            self._accumulator.get_single_pass_gatherer_key(
                self._current_debug_name.value
            )
        ]
        getattr(gatherer, hook_name)(layer_idx=self._current_layer_idx.value, **kwargs)

    def _reset(self):
        """Reset the expert distribution recorder."""
        logger.info("Resetting ExpertDistributionRecorder...")
        assert (
            self._current_layer_idx.value is None
        ), f"{self._current_layer_idx.value=}"
        for gatherer in self._single_pass_gatherers.values():
            gatherer.reset()
        self._accumulator.reset()

    def start_record(self):
        """Start recording the expert distribution."""
        if self._recording:
            logger.warning(
                "SGLang server is already recording expert ids. Did you forget to dump the expert ids recorded so far by sending requests to the `/stop_expert_distribution_record` and `/dump_expert_distribution_record` endpoints?"
            )
        self._reset()
        self._recording = True

    def stop_record(self):
        """Stop recording the expert distribution."""
        if not self._recording:
            logger.warning(
                "SGLang server has not been recording expert ids. Did you forget to start recording by sending request to the `/start_expert_distribution_record` endpoint?"
            )
        self._recording = False

    def dump_record(self, output_mode: _OutputMode = "file"):
        """Dump the expert distribution record and reset the recorder after dumping."""
        output = self._accumulator.dump(output_mode=output_mode)
        self._reset()
        return output

    @property
    def recording(self):
        return self._recording


_global_expert_distribution_recorder: Optional[ExpertDistributionRecorder] = (
    _ExpertDistributionRecorderNoop()
)


def get_global_expert_distribution_recorder():
    return _global_expert_distribution_recorder


def set_global_expert_distribution_recorder(value):
    global _global_expert_distribution_recorder
    _global_expert_distribution_recorder = value


# --------------------------------------- SinglePassGatherer -----------------------------------------


class _SinglePassGatherer(ABC):
    @staticmethod
    def init_new(
        server_args: ServerArgs,
        expert_location_metadata: ExpertLocationMetadata,
        rank: int,
    ) -> "_SinglePassGatherer":
        if server_args.expert_distribution_recorder_mode == "per_token":
            return _DetailSinglePassGatherer(
                server_args, expert_location_metadata, rank
            )

        if server_args.expert_distribution_recorder_mode == "stat_approx":
            if server_args.moe_a2a_backend != "none" and (
                server_args.deepep_mode == "normal"
            ):
                return _DeepepNormalSinglePassGatherer(expert_location_metadata, rank)
            else:
                raise NotImplementedError

        if server_args.moe_a2a_backend != "none":
            if server_args.deepep_mode == "normal":
                return _SelectExpertsSinglePassGatherer(expert_location_metadata, rank)
            elif server_args.deepep_mode == "low_latency":
                return _DeepepLowLatencySinglePassGatherer(
                    expert_location_metadata, rank
                )
            else:
                raise NotImplementedError

        return _SelectExpertsSinglePassGatherer(expert_location_metadata, rank)

    def __init__(self, expert_location_metadata: ExpertLocationMetadata, rank: int):
        self._expert_location_metadata = expert_location_metadata
        self._rank = rank

    def on_forward_pass_start(self, forward_batch: ForwardBatch):
        pass

    def on_select_experts(self, layer_idx: int, topk_ids: torch.Tensor):
        pass

    def on_deepep_dispatch_normal(
        self,
        layer_idx: int,
        local_physical_count_of_layer: List[int],
        num_tokens_per_rank,
        num_tokens_per_rdma_rank,
        num_tokens_per_expert,
    ):
        pass

    def on_deepep_dispatch_low_latency(
        self, layer_idx: int, local_physical_count_of_layer: torch.Tensor
    ):
        pass

    def reset(self):
        raise NotImplementedError

    def collect(self) -> Dict:
        raise NotImplementedError


class _DetailSinglePassGatherer(_SinglePassGatherer):
    # DeepSeek V3 has this value; should generalize later
    _TOP_K_NUM = 8

    def __init__(
        self,
        server_args: ServerArgs,
        expert_location_metadata: ExpertLocationMetadata,
        rank: int,
    ):
        super().__init__(expert_location_metadata, rank)
        self._metadata: Optional[Dict[str, Any]] = None
        self._topk_ids_of_layer = torch.zeros(
            (
                expert_location_metadata.num_layers,
                # TODO determine the max number
                server_args.chunked_prefill_size * 8,
                self._TOP_K_NUM,
            ),
            dtype=torch.int32,
            device=server_args.device,
        )
        self._misc_objects: List[Dict[str, Any]] = []
        assert (
            not server_args.enable_two_batch_overlap
        ), "DetailSinglePassGatherer does not support TBO yet"
        # TODO assert shared experts fusion is disabled, o/w data is wrong

    def on_forward_pass_start(self, forward_batch: ForwardBatch):
        assert self._metadata is None
        self._metadata = dict(
            # TODO pr-chain
            # rids=forward_batch.rids,
            input_ids=forward_batch.input_ids.cpu().tolist(),
            positions=forward_batch.positions.cpu().tolist(),
            extend_seq_lens=forward_batch.extend_seq_lens_cpu,
            forward_mode=forward_batch.forward_mode.value,
        )

    def on_select_experts(self, layer_idx: int, topk_ids: torch.Tensor):
        self._topk_ids_of_layer[layer_idx, : topk_ids.shape[0], : topk_ids.shape[1]] = (
            topk_ids
        )

    def on_deepep_dispatch_normal(
        self,
        layer_idx: int,
        local_physical_count_of_layer: List[int],
        num_tokens_per_rank,
        num_tokens_per_rdma_rank,
        num_tokens_per_expert,
    ):
        self._misc_objects.append(
            dict(
                layer_id=layer_idx,
                num_tokens_per_rank=num_tokens_per_rank.cpu().tolist(),
                num_tokens_per_rdma_rank=num_tokens_per_rdma_rank.cpu().tolist(),
                num_tokens_per_expert=num_tokens_per_expert.cpu().tolist(),
            )
        )

    def reset(self):
        self._topk_ids_of_layer[...] = -1
        self._misc_objects.clear()
        self._metadata = None

    def collect(self) -> Dict:
        num_tokens = len(self._metadata["input_ids"])

        global_physical_count = _convert_per_token_to_global_physical_count(
            num_tokens,
            num_layers=self._expert_location_metadata.num_layers,
            num_physical_experts=self._expert_location_metadata.num_physical_experts,
            _topk_ids_of_layer=self._topk_ids_of_layer,
        )

        return dict(
            **self._metadata,
            topk_ids_of_layer=self._topk_ids_of_layer[:, :num_tokens, :].clone().cpu(),
            misc_objects=self._misc_objects,
            global_physical_count=global_physical_count,
        )


class _LayerBasedCpuSinglePassGatherer(_SinglePassGatherer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._objects_of_layer = {}

    def _on_layer_data(self, layer_idx: int, objects: List[int]):
        assert 0 <= layer_idx < self._expert_location_metadata.num_layers
        if layer_idx in self._objects_of_layer:
            self._objects_of_layer[layer_idx] = _list_sum(
                self._objects_of_layer[layer_idx], objects
            )
        else:
            self._objects_of_layer[layer_idx] = objects

    def reset(self):
        self._objects_of_layer.clear()

    def _collect_objects(self, pad_len: int) -> torch.Tensor:
        data = [
            self._objects_of_layer.get(layer_index) or ([0] * pad_len)
            for layer_index in range(self._expert_location_metadata.num_layers)
        ]
        return torch.tensor(data)


def _list_sum(a: List, b: List) -> List:
    return [x + y for x, y in zip(a, b, strict=True)]


class _LayerBasedGpuSinglePassGatherer(_SinglePassGatherer):
    def __init__(self, *args, enable_global_physical_experts: bool, **kwargs):
        super().__init__(*args, **kwargs)
        self._enable_global_physical_experts = enable_global_physical_experts
        self._data = torch.zeros(
            (
                self._expert_location_metadata.num_layers,
                (
                    self._expert_location_metadata.num_physical_experts
                    if enable_global_physical_experts
                    else self._expert_location_metadata.num_local_physical_experts
                ),
            ),
            dtype=torch.int,
            device="cuda",
        )

    def reset(self):
        self._data[...] = 0

    def collect(self) -> Dict:
        if self._enable_global_physical_experts:
            global_physical_count = self._data
        else:
            # Can optimize if bottleneck
            global_physical_count = _convert_local_to_global_physical_count(
                self._data,
                rank=self._rank,
                num_local_physical_experts=self._expert_location_metadata.num_local_physical_experts,
                num_physical_experts=self._expert_location_metadata.num_physical_experts,
            )

        return dict(global_physical_count=global_physical_count)


class _SelectExpertsSinglePassGatherer(_LayerBasedGpuSinglePassGatherer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs, enable_global_physical_experts=True)

    # can optimize (e.g. fuse / compile)
    def on_select_experts(self, layer_idx: int, topk_ids: torch.Tensor):
        topk_ids = topk_ids.flatten()
        mask = topk_ids != -1
        self._data[layer_idx, :].scatter_add_(
            dim=0, index=topk_ids.masked_fill(~mask, 0).long(), src=mask.int()
        )


class _DeepepNormalSinglePassGatherer(_LayerBasedCpuSinglePassGatherer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        if torch.distributed.get_rank() == 0:
            logger.info(
                "DeepepNormalSinglePassGatherer gathers approximate statistics. "
                "If used with small batch size, consider using expert_distribution_recorder_mode=stat."
            )

    def on_deepep_dispatch_normal(
        self,
        layer_idx: int,
        local_physical_count_of_layer: List[int],
        num_tokens_per_rank,
        num_tokens_per_rdma_rank,
        num_tokens_per_expert,
    ):
        assert isinstance(local_physical_count_of_layer, list)
        self._on_layer_data(layer_idx, local_physical_count_of_layer)

    def collect(self) -> Dict:
        local_physical_count = super()._collect_objects(
            pad_len=self._expert_location_metadata.num_local_physical_experts
        )
        global_physical_count = _convert_local_to_global_physical_count(
            local_physical_count,
            rank=self._rank,
            num_local_physical_experts=self._expert_location_metadata.num_local_physical_experts,
            num_physical_experts=self._expert_location_metadata.num_physical_experts,
        )
        return dict(global_physical_count=global_physical_count)


class _DeepepLowLatencySinglePassGatherer(_LayerBasedGpuSinglePassGatherer):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs, enable_global_physical_experts=False)

    def on_deepep_dispatch_low_latency(
        self, layer_idx: int, local_physical_count_of_layer: torch.Tensor
    ):
        # Most naive implementation, can optimize later
        self._data[layer_idx, :] += local_physical_count_of_layer


def _convert_per_token_to_global_physical_count(
    num_tokens: int,
    num_layers: int,
    num_physical_experts: int,
    _topk_ids_of_layer: torch.Tensor,
) -> torch.Tensor:
    topk_ids_layer_major = _topk_ids_of_layer[:, :num_tokens, :].reshape(num_layers, -1)
    mask = topk_ids_layer_major != -1

    index = topk_ids_layer_major.masked_fill(~mask, 0).long()
    src = mask.int()

    ans = torch.zeros(
        (num_layers, num_physical_experts),
        dtype=_topk_ids_of_layer.dtype,
        device=_topk_ids_of_layer.device,
    )
    ans.scatter_add_(dim=1, index=index, src=src)
    return ans


def _convert_local_to_global_physical_count(
    local_physical_count: torch.Tensor,
    rank: int,
    num_local_physical_experts: int,
    num_physical_experts: int,
) -> torch.Tensor:
    dtype = local_physical_count.dtype
    device = local_physical_count.device
    num_layers, _ = local_physical_count.shape

    ans = torch.zeros((num_layers, num_physical_experts), dtype=dtype, device=device)
    ans[
        :, num_local_physical_experts * rank : num_local_physical_experts * (rank + 1)
    ] = local_physical_count
    return ans


# --------------------------------------- Accumulator -----------------------------------------

_SINGLE_PASS_GATHERER_KEY_PRIMARY = "primary"


class _Accumulator(ABC):
    @staticmethod
    def init_new(
        server_args: ServerArgs,
        expert_location_metadata: ExpertLocationMetadata,
        rank: int,
    ) -> "_Accumulator":
        return _Accumulator.get_class(server_args)(
            server_args, expert_location_metadata, rank
        )

    @staticmethod
    def get_class(server_args: ServerArgs) -> Type["_Accumulator"]:
        return {
            "stat": _StatAccumulator,
            "stat_approx": _StatAccumulator,
            "per_pass": _DetailAccumulator,
            "per_token": _DetailAccumulator,
        }[server_args.expert_distribution_recorder_mode]

    def __init__(
        self,
        server_args: ServerArgs,
        expert_location_metadata: ExpertLocationMetadata,
        rank: int,
    ):
        self._server_args = server_args
        self._expert_location_metadata = expert_location_metadata
        self._rank = rank

    def get_single_pass_gatherer_keys(self):
        return [_SINGLE_PASS_GATHERER_KEY_PRIMARY]

    def get_single_pass_gatherer_key(self, debug_name: Optional[str]):
        return _SINGLE_PASS_GATHERER_KEY_PRIMARY

    def append(
        self,
        forward_pass_id: int,
        gatherer_key: str,
        single_pass_data: Dict,
    ):
        pass

    def reset(self):
        pass

    def dump(self, output_mode: _OutputMode):
        pass


class _UtilizationRateAccumulatorMixin(_Accumulator):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)

        self._enable = self._server_args.enable_expert_distribution_metrics

        if self._enable:
            self.window_sizes = [10, 100, 1000]
            self._history = _DequeCollection(maxlens=self.window_sizes)
            self._rank = torch.distributed.get_rank()
            self._expert_dispatch_collector = ExpertDispatchCollector(
                self._expert_location_metadata.ep_size
            )
            self._collection_counter = 0

    def append(
        self,
        forward_pass_id: int,
        gatherer_key: str,
        single_pass_data: Dict,
    ):
        super().append(forward_pass_id, gatherer_key, single_pass_data)
        if self._enable:
            self._append_utilization_rate(
                forward_pass_id, single_pass_data["global_physical_count"]
            )

    def reset(self):
        super().reset()
        if self._enable:
            self._history.clear()

    def _append_utilization_rate(
        self, forward_pass_id: int, single_pass_global_physical_count: torch.Tensor
    ):
        gpu_physical_count = compute_gpu_physical_count(
            single_pass_global_physical_count,
            num_gpu=self._expert_location_metadata.ep_size,
        )
        gpu_physical_count = gpu_physical_count.to(self._server_args.device)
        torch.distributed.reduce(
            gpu_physical_count, dst=0, op=torch.distributed.ReduceOp.SUM
        )

        if self._rank == 0:
            self._collect_metrics_if_needed(gpu_physical_count)

            utilization_rate_tensor = compute_utilization_rate(gpu_physical_count)
            utilization_rate = torch.mean(utilization_rate_tensor).item()
            self._history.append(utilization_rate)

            gpu_physical_count_sum = gpu_physical_count.sum().item()

            logger.info(
                f"[Expert Balancedness] "
                f"forward_pass_id={forward_pass_id} "
                f"current_pass_balancedness={utilization_rate:.03f} "
                f"{''.join(f'last_{size}_average_balancedness={value:.03f} ' for size, value in self._history.mean().items())} "
                f"gpu_physical_count_sum={gpu_physical_count_sum}"
                # f"current_pass_per_layer={[round(x, 2) for x in utilization_rate_tensor.cpu().tolist()]}"
            )

    def _collect_metrics_if_needed(self, gpu_physical_count: torch.Tensor):
        # sglang:eplb_gpu_physical_count metric is disabled if SGLANG_EPLB_HEATMAP_COLLECTION_INTERVAL <= 0
        interval = get_int_env_var("SGLANG_EPLB_HEATMAP_COLLECTION_INTERVAL", 0)
        if interval > 0 and self._collection_counter % interval == 0:
            for layer_idx in range(self._expert_location_metadata.num_layers):
                count_of_layer = (
                    self._expert_dispatch_collector.eplb_gpu_physical_count.labels(
                        layer=str(layer_idx)
                    )
                )
                # Exclude the +Inf bucket.
                assert (
                    self._expert_location_metadata.ep_size
                    == len(count_of_layer._buckets) - 1
                ), f"{self._expert_location_metadata.ep_size=}, {len(count_of_layer._buckets)=}"
                for gpu_rank in range(self._expert_location_metadata.ep_size):
                    count = gpu_physical_count[layer_idx, gpu_rank]
                    if count > 0:
                        count_of_layer._sum.inc(count * gpu_rank)
                        count_of_layer._buckets[gpu_rank].inc(count)
        self._collection_counter += 1


class _DequeCollection:
    def __init__(self, maxlens: List[int]):
        self._dequeues = [deque(maxlen=maxlen) for maxlen in maxlens]

    def append(self, value):
        for d in self._dequeues:
            d.append(value)

    def clear(self):
        for d in self._dequeues:
            d.clear()

    def mean(self) -> Dict[int, float]:
        return {d.maxlen: sum(d) / len(d) for d in self._dequeues}


class _DetailAccumulator(_UtilizationRateAccumulatorMixin):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._records = []

    def get_single_pass_gatherer_keys(self):
        if False:  # TODO `server_args.enable_two_batch_overlap`
            return [_SINGLE_PASS_GATHERER_KEY_PRIMARY, "child_a", "child_b"]
        return super().get_single_pass_gatherer_keys()

    def get_single_pass_gatherer_key(self, debug_name: Optional[str]):
        if False:  # TODO `server_args.enable_two_batch_overlap`
            return debug_name or _SINGLE_PASS_GATHERER_KEY_PRIMARY
        return super().get_single_pass_gatherer_key(debug_name)

    def append(
        self,
        forward_pass_id: int,
        gatherer_key: str,
        single_pass_data: Dict,
    ):
        super().append(forward_pass_id, gatherer_key, single_pass_data)

        def _process_object(obj):
            if isinstance(obj, torch.Tensor):
                return obj.cpu().clone()
            return obj

        single_pass_data_processed = {
            k: _process_object(v) for k, v in single_pass_data.items()
        }

        self._records.append(
            dict(
                forward_pass_id=forward_pass_id,
                rank=self._rank,
                gatherer_key=gatherer_key,
                **single_pass_data_processed,
            )
        )

    def reset(self):
        super().reset()
        self._records.clear()

    def dump(self, output_mode: _OutputMode):
        assert output_mode == "file"
        output = dict(
            records=self._records,
            # NOTE: This may change during recording, so here we say it is the "last" one
            last_physical_to_logical_map=self._expert_location_metadata.physical_to_logical_map,
        )
        _dump_to_file(
            f"expert_distribution_recorder_{time.time()}_{self._rank}.pt", output
        )


class _StatAccumulator(_UtilizationRateAccumulatorMixin):
    def __init__(self, *args, **kwargs):
        super().__init__(*args, **kwargs)
        self._global_physical_count_of_buffered_step = _Buffer.init_new(
            item_shape=(
                self._expert_location_metadata.num_layers,
                # Cannot use local_physical_count to support select_experts
                self._expert_location_metadata.num_physical_experts,
            ),
            buffer_size=self._server_args.expert_distribution_recorder_buffer_size,
            dtype=torch.int32,
            device=self._server_args.device,
        )
        self._first_dump = True

    def append(
        self,
        forward_pass_id: int,
        gatherer_key: str,
        single_pass_data: Dict,
    ):
        super().append(forward_pass_id, gatherer_key, single_pass_data)
        # Can optimize if overhead here is large
        self._global_physical_count_of_buffered_step.append(
            single_pass_data["global_physical_count"]
        )

    def reset(self):
        super().reset()
        self._global_physical_count_of_buffered_step.reset()

    def dump(self, output_mode: _OutputMode):
        logical_count_of_buffered_step = _convert_global_physical_count_to_logical_count(
            self._global_physical_count_of_buffered_step.get_all(),
            num_layers=self._expert_location_metadata.num_layers,
            num_logical_experts=self._expert_location_metadata.num_logical_experts,
            physical_to_logical_map=self._expert_location_metadata.physical_to_logical_map,
        )

        if self._first_dump:
            self._first_dump = False
            torch.get_device_module().empty_cache()

        torch.distributed.all_reduce(
            logical_count_of_buffered_step, op=torch.distributed.ReduceOp.SUM
        )

        output = dict(
            rank=self._rank,
            logical_count=logical_count_of_buffered_step,
            average_utilization_rate_over_window=self._get_global_average_utilization_rate(),
        )

        if output_mode == "file":
            if self._rank == 0:
                _dump_to_file(f"expert_distribution_recorder_{time.time()}.pt", output)
        elif output_mode == "object":
            return output
        else:
            raise NotImplementedError

    def _get_global_average_utilization_rate(self):
        if not self._enable or math.isclose(
            self._server_args.eplb_min_rebalancing_utilization_threshold, 1.0
        ):
            return None

        if self._rank == 0:
            utilization_mean_rates = self._history.mean()
            window_index = self.window_sizes[-1]
            average_utilization_rate_over_window = (
                utilization_mean_rates[window_index]
                if window_index in utilization_mean_rates
                else 0
            )

            avg_rate_tensor = torch.tensor(
                [average_utilization_rate_over_window],
                dtype=torch.float32,
                device="cuda",
            )
        else:
            avg_rate_tensor = torch.empty(1, dtype=torch.float32, device="cuda")
        torch.distributed.broadcast(avg_rate_tensor, src=0)
        return avg_rate_tensor.item()


def _dump_to_file(name, data):
    save_dir = Path(envs.SGLANG_EXPERT_DISTRIBUTION_RECORDER_DIR.get())
    path_output = save_dir / name
    logger.info(f"Write expert distribution to {path_output}")
    if not save_dir.exists():
        save_dir.mkdir(parents=True, exist_ok=True)
    torch.save(data, str(path_output))


class _Buffer:
    @staticmethod
    def init_new(item_shape: Tuple, buffer_size: int, dtype, device):
        if buffer_size < 0:
            return _InfiniteBuffer(item_shape, dtype=dtype, device=device)
        else:
            return _CircularBuffer(item_shape, buffer_size, dtype=dtype, device=device)

    def append(self, value: torch.Tensor):
        raise NotImplementedError

    def get_all(self) -> torch.Tensor:
        raise NotImplementedError

    def reset(self):
        raise NotImplementedError


class _CircularBuffer(_Buffer):
    def __init__(self, item_shape: Tuple, buffer_size: int, dtype, device):
        self._buffer = torch.zeros(
            (buffer_size, *item_shape), dtype=dtype, device=device
        )
        self._curr_index = 0

    def append(self, value: torch.Tensor):
        self._buffer[self._curr_index] = value
        self._curr_index = (self._curr_index + 1) % len(self._buffer)

    def get_all(self) -> torch.Tensor:
        return self._buffer

    def reset(self):
        self._buffer[...] = 0


class _InfiniteBuffer(_Buffer):
    def __init__(self, item_shape: Tuple, dtype, device):
        self._item_shape = item_shape
        self._buffer = torch.zeros((128, *item_shape), dtype=dtype, device=device)
        self._size = 0

    def append(self, value: torch.Tensor):
        curr_buffer_size = len(self._buffer)
        dtype = self._buffer.dtype
        device = self._buffer.device

        if self._size == curr_buffer_size:
            new_buffer = torch.zeros(
                (2 * curr_buffer_size, *self._item_shape), dtype=dtype, device=device
            )
            new_buffer[:curr_buffer_size] = self._buffer
            self._buffer = new_buffer

        self._buffer[self._size] = value
        self._size += 1

    def get_all(self) -> torch.Tensor:
        return self._buffer[: self._size]

    def reset(self):
        self._buffer[...] = 0
        self._size = 0


def _convert_global_physical_count_to_logical_count(
    # (whatever, num_layers, num_physical_experts)
    global_physical_count: torch.Tensor,
    num_layers: int,
    num_logical_experts: int,
    physical_to_logical_map: torch.Tensor,
):
    dim_extra, _, _ = global_physical_count.shape
    dtype = global_physical_count.dtype
    device = global_physical_count.device
    logical_count = torch.zeros(
        (dim_extra, num_layers, num_logical_experts), dtype=dtype, device=device
    )
    logical_count.scatter_add_(
        dim=2,
        index=physical_to_logical_map.unsqueeze(0)
        .expand(dim_extra, -1, -1)
        .to(torch.int64),
        src=global_physical_count,
    )
    return logical_count


def compute_gpu_physical_count(
    physical_count_of_whatever: torch.Tensor,  # (..., num_layer, num_physical_expert)
    num_gpu: int,
):
    """output: gpu_physical_count_of_batch (..., num_layer, num_gpu)"""
    return einops.reduce(
        physical_count_of_whatever,
        "... num_layer (num_gpu num_expert_per_gpu) -> ... num_layer num_gpu",
        "sum",
        num_gpu=num_gpu,
    )


def compute_utilization_rate(
    gpu_physical_count_of_batch: torch.Tensor,  # (..., num_layer, num_gpu)
):
    """output: utilization_rate (..., num_layer)"""
    gpu_physical_count_of_batch = gpu_physical_count_of_batch.float()
    max_gpu_physical_count = einops.reduce(
        gpu_physical_count_of_batch,
        "... num_layer num_gpu -> ... num_layer",
        "max",
    )
    avg_gpu_physical_count = einops.reduce(
        gpu_physical_count_of_batch,
        "... num_layer num_gpu -> ... num_layer",
        "mean",
    )
    return (avg_gpu_physical_count + 1e-5) / (max_gpu_physical_count + 1e-5)
